""" DataTable.py INTRODUCTION This class is useful for representing a table of data arranged by named columns, where each row in the table can be thought of as a record: name phoneNumber ------ ----------- Chuck 893-3498 Bill 893-0439 John 893-5901 This data often comes from delimited text files which typically have well defined columns or fields with several rows each of which can be thought of as a record. Using a DataTable can be as easy as using lists and dictionaries: table = DataTable('users.csv') for row in table: print row['name'], row['phoneNumber'] Or even: table = DataTable('users.csv') for row in table: print '%(name)s %(phoneNumber)s' % row The above print statement relies on the fact that rows can be treated like dictionaries, using the column headings as keys. You can also treat a row like an array: table = DataTable('something.tabbed', delimiter='\t') for row in table: for item in row: print item, print COLUMNS Column headings can have a type specification like so: name, age:int, zip:int Possible types include string, int, float and datetime. However, datetime is not well supported right now. String is assumed if no type is specified but you can set that assumption when you create the table: table = DataTable(headings, defaultType='float') Using types like int and float will cause DataTable to actually convert the string values (perhaps read from a file) to these types so that you can use them in natural operations. For example: if row['age']>120: self.flagData(row, 'age looks high') As you can see, each row can be accessed as a dictionary with keys according the column headings. Names are case sensitive. ADDING ROWS Like Python lists, data tables have an append() method. You can append TableRecords, or you pass a dictionary, list or object, in which case a TableRecord is created based on given values. See the method docs below for more details. FILES By default, the files that DataTable reads from are expected to be comma-separated value files. Limited comments are supported: A comment is any line whose very first character is a #. This allows you to easily comment out lines in your data files without having to remove them. Whitespace around field values is stripped. You can control all this behavior through the arguments found in the initializer and the various readFoo() methods: ...delimiter=',', allowComments=1, stripWhite=1 For example: table = DataTable('foo.tabbed', delimiter='\t', allowComments=0, stripWhite=0) You should access these parameters by their name since additional ones could appear in the future, thereby changing the order. If you are creating these text files, we recommend the comma-separated-value format, or CSV. This format is better defined than the tab delimited format, and can easily be edited and manipulated by popular spreadsheets and databases. TABLES FROM SCRATCH Here's an example that constructs a table from scratch: table = DataTable(['name', 'age:int']) table.append(['John', 80]) table.append({'name': 'John', 'age': 80}) print table QUERIES A simple query mechanism is supported for equality of fields: matches = table.recordsEqualTo({'uid': 5}) if matches: for match in matches: print match else: print 'No matches.' COMMON USES * Programs can keep configuration and other data in simple comma- separated text files and use DataTable to access them. For example, a web site could read it's sidebar links from such a file, thereby allowing people who don't know Python (or even HTML) to edit these links without having to understand other implementation parts of the site. * Servers can use DataTable to read and write log files. FROM THE COMMAND LINE The only purpose in invoking DataTable from the command line is to see if it will read a file: > python DataTable.py foo.csv The data table is printed to stdout. CACHING DataTable uses "pickle caching" so that it can read .csv files faster on subsequent loads. You can disable this across the board with: from MiscUtils.DataTable import DataTable DataTable.usePickleCache = 0 Or per instance by passing "usePickleCache=0" to the constructor. See the docstring of PickleCache.py for more information. MORE DOCS Some of the methods in this module have worthwhile doc strings to look at. See below. TO DO * Allow callback parameter or setting for parsing CSV records. * Perhaps TableRecord should inherit UserList and UserDict and override methods as appropriate...? * Better support for datetime. * _types and BlankValues aren't really packaged, advertised or documented for customization by the user of this module. * DataTable: * Parameterize the TextColumn class. * Parameterize the TableRecord class. * More list-like methods such as insert() * writeFileNamed() is flawed: it doesn't write the table column type * Should it inherit from UserList? * Add error checking that a column name is not a number (which could cause problems). * Look for various @@ tags through out the code. """ import string, sys from CSVParser import CSVParser from string import join, replace, split, strip from types import * from MiscUtils import NoDefault try: from mx.DateTime import DateTimeType, DateTimeFrom except ImportError: pass ## Types ## DateTimeType = "<custom-type 'datetime'>" ObjectType = "<type 'Object'>" _types = { 'string': StringType, 'int': IntType, 'long': LongType, 'float': FloatType, 'datetime': DateTimeType, 'object': ObjectType, } ## Classes ## class DataTableError(Exception): pass class TableColumn: """ A table column represents a column of the table including name and type. It does not contain the actual values of the column. These are stored individually in the rows. """ def __init__(self, spec): # spec is a string such as 'name' or 'name:type' fields = split(spec, ':') if len(fields)>2: raise DataTableError, 'Invalid column spec %s' % repr(spec) self._name = fields[0] if len(fields)==1: self._type = None else: self.setType(fields[1]) def name(self): return self._name def type(self): return self._type def setType(self, type): """ Sets the type (by a string containing the name) of the heading. Usually invoked by DataTable to set the default type for columns whose types were not specified. """ if type==None: self._type = None else: try: self._type = _types[type] except: raise DataTableError, 'Unknown type %s' % repr(type) def __repr__(self): return '<TableColumn %s with %s at %x>' % ( repr(self._name), repr(self._type), id(self)) def __str__(self): return self._name ## Utilities ## def valueForRawValue(self, rawValue): """ The rawValue is typically a string or value already of the appropriate type. TableRecord invokes this method to ensure that values (especially strings that come from files) are the correct types (e.g., ints are ints and floats are floats). """ # @@ 2000-07-23 ce: an if-else ladder? perhaps these should be dispatched messages or a class hier if self._type is StringType: value = str(rawValue) elif self._type is IntType: if rawValue=='': value = 0 else: value = int(rawValue) elif self._type is LongType: if rawValue=='': value = 0 else: value = long(rawValue) elif self._type is FloatType: if rawValue=='': value = 0.0 else: value = float(rawValue) elif self._type is DateTimeType: value = DateTimeFrom(rawValue) elif self._type is ObjectType: value = rawValue else: raise DataTableError, 'Unknown column type "%s"' % self._type return value class DataTable: """ See the doc string for this module. """ usePickleCache = 1 ## Init ## def __init__(self, filenameOrHeadings=None, delimiter=',', allowComments=1, stripWhite=1, defaultType='string', usePickleCache=None): if usePickleCache is None: self.usePickleCache = self.usePickleCache # grab the class-level attr else: self.usePickleCache = usePickleCache if not _types.has_key(defaultType): raise DataTableError, 'Unknown type for default type: %s' % repr(defaultType) self._defaultType = defaultType self._filename = None self._headings = [] self._rows = [] if filenameOrHeadings: if type(filenameOrHeadings) is StringType: self.readFileNamed(filenameOrHeadings, delimiter, allowComments, stripWhite) else: self.setHeadings(filenameOrHeadings) ## File I/O ## def readFileNamed(self, filename, delimiter=',', allowComments=1, stripWhite=1): self._filename = filename data = None if self.usePickleCache: from PickleCache import readPickleCache, writePickleCache data = readPickleCache(filename, pickleVersion=1, source='MiscUtils.DataTable') if data is None: file = open(self._filename, 'r') self.readFile(file, delimiter, allowComments, stripWhite) file.close() if self.usePickleCache: writePickleCache(self, filename, pickleVersion=1, source='MiscUtils.DataTable') else: self.__dict__ = data.__dict__ return self def readFile(self, file, delimiter=',', allowComments=1, stripWhite=1): return self.readLines(file.readlines(), delimiter, allowComments, stripWhite) def readString(self, string, delimiter=',', allowComments=1, stripWhite=1): return self.readLines(split(string, '\n'), delimiter, allowComments, stripWhite) def readLines(self, lines, delimiter=',', allowComments=1, stripWhite=1): haveReadHeadings = 0 parse = CSVParser(fieldSep=delimiter, allowComments=allowComments, stripWhitespace=stripWhite).parse for line in lines: # process a row, either headings or data values = parse(line) if values: if haveReadHeadings: row = TableRecord(self, values) self._rows.append(row) else: self.setHeadings(values) self.createNameToIndexMap() haveReadHeadings = 1 if values is None: raise DataTableError, "Unfinished multiline record." return self def save(self): self.writeFileNamed(self._filename) def writeFileNamed(self, filename): file = open(filename, 'w') self.writeFile(file) file.close() def writeFile(self, file): """ @@ 2000-07-20 ce: This doesn't write the column types (like :int) back out. @@ 2000-07-21 ce: It's notable that a blank numeric value gets read as zero and written out that way. Also, values None are written as blanks. """ # write headings file.write(join(map(lambda h: str(h), self._headings), ',')) file.write('\n') def ValueWritingMapper(item): # So that None gets written as a blank and everything else as a string if item is None: return '' else: return str(item) # write rows for row in self._rows: file.write(join(map(ValueWritingMapper, row), ',')) file.write('\n') def commit(self): if self._changed: self.save() self._changed = 0 ## Headings ## def heading(self, index): if type(key) is StringType: key = self._nameToIndexMap[key] return self._headings[index] def hasHeading(self, name): return self._nameToIndexMap.has_key(name) def numHeadings(self): return len(self._headings) def headings(self): return self._headings def setHeadings(self, headings): """ Headings can be a list of strings (like ['name', 'age:int']) or a list of TableColumns or None. """ if not headings: self._headings = [] elif type(headings[0]) is StringType: self._headings = map(lambda h: TableColumn(h), headings) elif isinstance(headings[0], TableColumn): self._headings = list(headings) for heading in self._headings: if heading.type() is None: heading.setType(self._defaultType) self.createNameToIndexMap() ## Row access (list like) ## def __len__(self): return len(self._rows) def __getitem__(self, index): return self._rows[index] def append(self, object): """ If object is not a TableRecord, then one is created, passing the object to initialize the TableRecord. Therefore, object can be a TableRecord, list, dictionary or object. See TableRecord for details. """ if not isinstance(object, TableRecord): object = TableRecord(self, object) self._rows.append(object) self._changed = 1 ## Queries ## def recordsEqualTo(self, dict): records = [] keys = dict.keys() for record in self._rows: matches = 1 for key in keys: if record[key]!=dict[key]: matches = 0 break if matches: records.append(record) return records ## As a string ## def __repr__(self): # Initial info s = ['DataTable: %s\n%d rows\n' % (self._filename, len(self._rows))] # Headings s.append(' ') s.append(join(map(lambda h: str(h), self._headings), ', ')) s.append('\n') # Records i = 0 for row in self._rows: s.append('%3d. ' % i) s.append(join(map(lambda r: str(r), row), ', ')) s.append('\n') i = i + 1 return join(s, '') ## As a dictionary ## def dictKeyedBy(self, key): """ Returns a dictionary containing the contents of the table indexed by the particular key. This is useful for tables that have a column which represents a unique key (such as a name, serial number, etc.). """ dict = {} for row in self: dict[row[key]] = row return dict ## Misc access ## def filename(self): return self._filename def nameToIndexMap(self): """ Table rows keep a reference to this map in order to speed up index-by-names (as in row['name']). """ return self._nameToIndexMap ## Self utilities ## def createNameToIndexMap(self): """ Invoked by self to create the nameToIndexMap after the table's headings have been read/initialized. """ map = {} for i in range(len(self._headings)): map[self._headings[i].name()] = i self._nameToIndexMap = map # @@ 2000-07-20 ce: perhaps for each type we could specify a function to convert from string values to the values of the type BlankValues = { StringType: '', IntType: 0, FloatType: 0.0, DateTimeType: '', } class TableRecord: ## Init ## def __init__(self, table, values=None): """ Dispatches control to one of the other init methods based on the type of values. Values can be one of three things: 1. A TableRecord 2. A list 3. A dictionary 4. Any object responding to hasValueForKey() and valueForKey(). """ self._headings = table.headings() self._nameToIndexMap = table.nameToIndexMap() # @@ 2000-07-20 ce: Take out the headings arg to the init method since we have an attribute for that if values is not None: valuesType = type(values) if valuesType is ListType or valuesType is TupleType: # @@ 2000-07-20 ce: check for required attributes instead self.initFromSequence(values) elif valuesType is DictType: self.initFromDict(values) elif valuesType is InstanceType: self.initFromObject(value) else: raise DataTableError, 'Unknown type for values %s.' % valuesType def initFromSequence(self, values): if len(self._headings)<len(values): raise DataTableError, ('There are more values than headings.\nheadings(%d, %s)\nvalues(%d, %s)' % (len(self._headings), self._headings, len(values), values)) self._values = [] numHeadings = len(self._headings) numValues = len(values) assert numValues<=numHeadings for i in range(numHeadings): heading = self._headings[i] if i>=numValues: self._values.append(BlankValues[heading.type()]) else: self._values.append(heading.valueForRawValue(values[i])) def initFromDict(self, dict): self._values = [] for heading in self._headings: name = heading.name() if dict.has_key(name): self._values.append(heading.valueForRawValue(dict[name])) else: self._values.append(BlankValues[heading.type()]) def initFromObject(self, object): """ The object is expected to response to hasValueForKey(name) and valueForKey(name) for each of the headings in the table. It's alright if the object returns 0 for hasValueForKey(). In that case, a "blank" value is assumed (such as zero or an empty string). If hasValueForKey() returns 1, then valueForKey() must return a value. """ self._values = [] for heading in self._headings: name = heading.name() if object.hasValueForKey(name): self._values.append(heading.valueForRawValue(object.valueForKey(name))) else: self._values.append(BlankValues[heading.type()]) ## Accessing like a sequence or dictionary ## def __len__(self): return len(self._values) def __getitem__(self, key): if type(key) is StringType: key = self._nameToIndexMap[key] return self._values[key] def __setitem__(self, key, value): if type(key) is StringType: key = self._nameToIndexMap[key] self._values[key] = value def __repr__(self): return '%s' % self._values def get(self, key, default=None): index = self._nameToIndexMap.get(key, None) if index is None: return default else: return self._values[index] def has_key(self, key): return self._nameToIndexMap.has_key(key) def keys(self): return self._nameToIndexMap.keys() def values(self): return self._values def items(self): items = [] for key in self.keys(): items.append((key, self[key])) return items ## Additional access ## def asList(self): """ Returns a sequence whose values are the same at the record's and in the order defined by the table. """ # It just so happens that our implementation already has this return self._values[:] def asDict(self): """ Returns a dictionary whose key-values match the table record. """ dict = {} nameToIndexMap = self._nameToIndexMap for key in nameToIndexMap.keys(): dict[key] = self._values[nameToIndexMap[key]] return dict ## valueForFoo() family ## def valueForKey(self, key, default=NoDefault): if default is NoDefault: return self[key] else: return self.get(key, default) def valueForAttr(self, attr, default=NoDefault): return self.valueForKey(attr['Name'], default) def main(args=None): if args is None: args = sys.argv for arg in args[1:]: dt = DataTable(arg) print '*** %s ***' % arg print dt print if __name__=='__main__': main()